How I Built an AI-Powered Reel & Short Maker in Swift: Handling Auto-Cutouts & Beat-Syncing Efficiently

Özlem Akalın — Fri, 14 Aug 2026 12:15:19 +0000

Hey DEV community! 👋

As an indie iOS developer, I’ve always been fascinated by short-form video algorithms and how visual retention works. Over the past few months, I noticed a major friction point for creators: creating high-retention video hooks (like stop-motion cutouts and precise beat-synced cuts) takes way too long in traditional editors.

To solve this, I built Reeliva — an iOS application designed to automate visual cutout effects, seamless looping, and audio-to-video syncing in seconds.

I wanted to share a few technical challenges I faced during development and how I tackled them in Swift.

🛠️ 1. Real-Time Auto-Cutout & Layering
One of the biggest hurdles was extracting subjects from video frames in real-time without causing heavy frame drops or overheating the device.

The Challenge: Processing high-resolution video frames on-device while maintaining a smooth UI preview.

The Solution: Leveraged Vision framework combined with Core Imageperformance shaders. By performing subject segmentation asynchronously on a background queue and caching render layers, I managed to keep the playback butter-smooth.

// A simplified glimpse into handling vision segmentation requests asynchronously
func processFrameSegmentation(pixelBuffer: CVPixelBuffer) {
    let request = VNGeneratePersonSegmentationRequest()
    request.qualityLevel = .balanced // Balancing speed vs accuracy

    let handler = VNImageRequestHandler(cvPixelBuffer: pixelBuffer, options: [:])
    DispatchQueue.global(qos: .userInitiated).async {
        try? handler.perform([request])
        guard let result = request.results?.first else { return }
        // Mask generation logic & rendering
    }
}

🎵 2. Precise Audio Beat-Syncing
Visual cuts hit hardest when they land exactly on the audio beat (often down to milliseconds).

Instead of forcing users to manually trim clips to match a waveform, Reeliva parses audio transient data to automatically map template transitions directly to the strongest beats of trending sounds.

🚀 3. Key Takeaways & Current Tech Stack
UI Framework: UIKit for ultra-responsive layout and custom paywall/template flows.

Core Video Processing: AVFoundation + Core Image + Vision.

Architecture: MVVM-C for clean navigation and decoupled video pipeline logic.

🎁 Free 1-Month Pro Access for the DEV Community!

Since this community has always been an amazing place for feedback, I’d love for fellow iOS devs and creators here to test Reeliva.

I’m giving away 50 Promo Codes for Free Pro Access!

Check out Reeliva on the App Store.

Drop a comment below with your feedback on the UX, performance, or video rendering speed.

I’ll send a Pro Promo Code directly to your inbox/replies!

Would love to hear your thoughts on how you handle heavy video rendering or media pipelines in your own iOS projects! 🚀

DEV Community: Özlem Akalın

How I Built an AI-Powered Reel & Short Maker in Swift: Handling Auto-Cutouts & Beat-Syncing Efficiently